Develop Your First Neural Network in Python With Keras Step-By-Step

It wraps the efficient numerical computation libraries Theano and TensorFlow and allows you to define and train neural network models in a few short lines of code.

The steps you are going to cover in this tutorial are as follows: This tutorial has a few requirements: If you need help with your environment, see the tutorial: Create a new file called keras_first_network.py and type or copy-and-paste the code into the file as you go.

This makes it easy to use directly with neural networks that expect numerical input and output values, and ideal for our first neural network in Keras.

We can specify the number of neurons in the layer as the first argument, the initialization method as the second argument as init and specify the activation function using the activation argument.

In this case, we initialize the network weights to a small random number generated from a uniform distribution (&#8216;uniform&#8216;), in this case between 0 and 0.05 because that is the default uniform weight initialization in Keras.

We use a sigmoid on the output layer to ensure our network output is between 0 and 1 and easy to map to either a probability of class 1 or snap to a hard classification of either class with a default threshold of 0.5.

The second hidden layer has 8 neurons and finally, the output layer has 1 neuron to predict the class (onset of diabetes or not).

The backend automatically chooses the best way to represent the network for training and making predictions to run on your hardware, such as CPU or GPU or even distributed.

We must specify the loss function to use to evaluate a set of weights, the optimizer used to search through different weights for the network and any optional metrics we would like to collect and report during training.

The training process will run for a fixed number of iterations through the dataset called epochs, that we must specify using the nepochs argument.

We can also set the number of instances that are evaluated before a weight update in the network is performed, called the batch size and set using the batch_size argument.

You can evaluate your model on your training dataset using the evaluate() function on your model and pass it the same input and output used to train the model.

This will generate a prediction for each input and output pair and collect scores, including the average loss and any metrics you have configured, such as accuracy.

Running this example, you should see a message for each of the 150 epochs printing the loss and accuracy for each, followed by the final evaluation of the trained model on the training dataset.

Specifically, you learned the five key steps in using Keras to create a neural network or deep learning model, step-by-step including: Do you have any questions about Keras or about this tutorial? Ask

One of our core aspirations at OpenAI is to develop algorithms and techniques that endow computers with an understanding of our world.

To train a generative model we first collect a large amount of data in some domain (e.g., think millions of images, sentences, or sounds, etc.) and then train a model to generate data like it.

The intuition behind this approach follows a famous quote from Richard Feynman: “What I cannot create, I do not understand.” The trick is that the neural networks we use as generative models have a number of parameters significantly smaller than the amount of data we train them on, so the models are forced to discover and efficiently internalize the essence of the data in order to generate it.

Suppose we have some large collection of images, such as the 1.2 million images in the ImageNet dataset (but keep in mind that this could eventually be a large collection of images or videos from the internet or robots).

This network takes as input 100 random numbers drawn from a uniform distribution (we refer to these as a code, or latent variables, in red) and outputs an image (in this case 64x64x3 images on the right, in green).

But in addition to that — and here's the trick — we can also backpropagate through both the discriminator and the generator to find how we should change the generator's parameters to make its 200 samples slightly more confusing for the discriminator.

These two networks are therefore locked in a battle: the discriminator is trying to distinguish real images from fake images and the generator is trying to create images that make the discriminator think they are real.

both cases the samples from the generator start out noisy and chaotic, and over time converge to have more plausible image statistics: This is exciting — these neural networks are learning what the visual world looks like!

Eventually, the model may discover many more complex regularities: that there are certain types of backgrounds, objects, textures, that they occur in certain likely arrangements, or that they transform in certain ways over time in videos, etc.

In the example image below, the blue region shows the part of the image space that, with a high probability (over some threshold) contains real images, and black dots indicate our data points (each is one image in our dataset).

Now, our model also describes a distribution \hat{p}_{\theta}(x) (green) that is defined implicitly by taking points from a unit Gaussian distribution (red) and mapping them through a (deterministic) neural network — our generative model (yellow).

Therefore, you can imagine the green distribution starting out random and then the training process iteratively changing the parameters \theta to stretch and squeeze it to better match the blue distribution.

First, as mentioned above GANs are a very promising family of generative models because, unlike other methods, they produce very clean and sharp images and learn codes that contain valuable information about these textures.

These techniques allow us to scale up GANs and obtain nice 128x128 ImageNet samples: Our CIFAR-10 samples also look very sharp - Amazon Mechanical Turk workers can distinguish our samples from real data with an error rate of 21.3% (50% would be random guessing): In addition to generating pretty pictures, we introduce an approach for semi-supervised learning with GANs that involves the discriminator producing an additional output indicating the label of the input.

On MNIST, for example, we achieve 99.14% accuracy with only 10 labeled examples per class with a fully connected neural network — a result that’s very close to the best known results with fully supervised approaches using all 60,000 labeled examples.

The core contribution of this work, termed inverse autoregressive flow (IAF), is a new approach that, unlike previous work, allows us to parallelize the computation of rich approximate posteriors, and make them almost arbitrarily flexible.

A regular GAN achieves the objective of reproducing the data distribution in the model, but the layout and organization of the code space is underspecified — there are many possible solutions to mapping the unit Gaussian to images and the one we end up with might be intricate and highly entangled.

It's clear from the five provided examples (along each row) that the resulting dimensions in the code capture interpretable dimensions, and that the model has perhaps understood that there are camera angles, facial variations, etc., without having been told that these features exist and are important: The next two recent projects are in a reinforcement learning (RL) setting (another area of focus at OpenAI), but they both involve a generative model component.